• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.69-85
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• 2019

BeiDou navigation satellite system (BDS) is one of the four main types of global navigation satellite systems. The current system has been widely used by the military and by the aerospace, transportation, and marine fields, among others. However, challenges still remain in the BeiDou system, which requires rapid responses for delay-sensitive devices. A differential positioning algorithm called the data center-based differential positioning (DCDP) method is widely used to avoid the influence of errors. In this method, the positioning information of multiple base stations is uploaded to the data center, and the positioning errors are calculated uniformly by the data center based on the minimum variance or a weighted average algorithm. However, the DCDP method has high delay and overload risk. To solve these problems, this paper introduces edge computing to relieve pressure on the data center. Instead of transmitting the positioning information to the data center, a novel method called edge computing-based differential positioning (ECDP) chooses the nearest reference station to perform edge computing and transmits the difference value to the mobile receiver directly. Simulation results and experiments demonstrate that the performance of the ECDP outperforms that of the DCDP method. The delay of the ECDP method is about 500ms less than that of the DCDP method. Moreover, in the range of allowable burst error, the median of the positioning accuracy of the ECDP method is 0.7923m while that of the DCDP method is 0.8028m.

• 대한지역사회영양학회지
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• 제24권2호
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• pp.117-126
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• 2019

Objectives: This study was conducted to investigate the current difficulties surrounding children's obesity management and evaluate the application of a mobile phone as a tool to overcome such difficulties of obesity management from the perspective of main caregivers of elementary school students. Methods: The qualitative data were collected through 3 focus group interviews including 6 full-time housewives, 7 mothers with overweight children, and 4 working mothers. Data were analyzed using a thematic approach. Results: The limitations of current children's obesity management included difficulty in diet management and exercise as well as challenges of setting goals and lack of support at the household and school levels. Mobile technology may be useful to overcome the current problems by providing real-time knowledge on diet management and physical activity, online compensation scheme according to goal setting, and interactive environmental supports at both household and school levels for promoting overall health. Conclusions: The mobile-based multiple support program may assist in overcoming the current limitations of child obesity management by providing tailored information and by creating a more supportive environment.

• 전기전자학회논문지
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• 제22권4호
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• pp.1068-1072
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• 2018

지상의 무선 통신 환경과 비교하여 수중 환경은 전력제한, 긴 전파지연, 낮은 전송율, 큰 전파손실 등 통신에 불리한 많은 제약적인 특성들이 존재한다. 전파의 신호 감쇠가 심하여 상대적으로 전송 손실이 낮은 음파를 이용하여 통신하게 되는데 음파의 수중 속도는 약 1,500m/s로 지상의 전파속도에 비해 매우 느린 속도를 가진다. 따라서, 지상 통신을 위해 제안된 기존의 MAC 프로토콜들은 바로 수중 통신에 적용될 수 없고 수중 환경에 적합하게 새로운 설계가 필요하다. 본 논문은 무선망의 대표적인 CSMA 기반 MAC 프로토콜들에 대해 수중 환경에서 그 성능을 비교 분석하여 수중통신에 적합한 MAC 프로토콜 설계를 위한 중요 고려 사항들을 제시하였다. 분석결과, 수중환경에서는 제어 패킷의 개수가 MAC 프로토콜의 성능에 큰 영향을 미친다는 것을 알 수 있었고, 이 결과는 수중 통신에 최적인 새로운 MAC 프로토콜을 제안하는 연구들에 기초 자료로 이용될 수 있을 것이다.

• Healthcare Informatics Research
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• 제24권4호
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• pp.381-386
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• 2018

Objectives: This study aimed to describe the process of utilizing a mobile application for ecological momentary assessment (EMA) to collect data on stress and mood in daily life setting. Methods: A mobile application for the Android operating system was developed and installed with a set of questions regarding momentary mood and stress into a smartphone of a participant. The application sets alarms at semi-random intervals in 60-minute blocks, four times a day for 7 days. After obtaining all momentary affect and stress, the questions to assess the usability of the mobile EMA application were also administered. Results: The data were collected from 97 police officers working in Gyeonggi Province of South Korea. The mean completion rate was 60.0% ranging from 3.5% to 100%. The means of positive and negative affect were 18.34 of 28 and 19.09 of 63. The mean stress was 17.92 of 40. Participants responded that the mobile application correctly measured their affect ($4.34{\pm}0.83$) and stress ($4.48{\pm}0.62$) of 5-point Likert scale. Conclusions: Our study investigated the process of utilizing a mobile application to assess momentary affect and stress at repeated times. We found challenges regarding adherence to the research protocol, such as completion and delay of answering after alarm notification. Despite this inherent issue of adherence to the research protocol, the EMA still has advantages of reducing recall bias and assessing the actual moment of interest at multiple time points that improves ecological validity.

• 공학교육연구
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• 제24권2호
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• pp.20-28
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• 2021

In 2020, the COVID-19 pandemic has brought dramatic changes in the field of engineering education. Contrary to the traditional engineering education emphasis on content-oriented, design-based, hands-on, experimental, and field experience, most of engineering classes in 2020 had to be undertaken remotely online. However, it has not been explored how professors and students perceive about such a shift in engineering education. The aim of the current study was to investigate the perceptions of professors and students on online classes in engineering colleges during spring Semester 2020. Questionnaire data were collected from 100 professors and 4,152 students in the college of engineering. The results of this study were as following: Students were less satisfied with the online classes than professors. The online lecture method that students were most satisfied with was the recorded lecture. This is likely due to the fact that the recorded lectures can be repeated multiple times anytime, anywhere. Moreover, the experimental classes, which conventionally has more of an emphasis on the hands-on experience, also had to be conducted remotely, showing even lower satisfaction among students. Most of professors reported that the average hours they spent on preparing for online lecture increased compared to face-to-face class. Both professors and students preferred in-person exam as a desirable method of end-of-semester assessment for grading. The results of the current study have important implications for the improvement of online course environments. It is important for professors to design a structured class suitable for online education and understand the challenges students encounter during online classes. Also, professors should communicate more openly about their expectations and rubrics for class goals and assignments. Schools also needs to make effort to provide the support for the internet environment of students.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권7호
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• pp.2496-2512
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• 2021

The requirements for powerful computing capability, high capacity, low latency and low energy consumption of emerging services, pose severe challenges to the fifth-generation (5G) network. As a promising paradigm, mobile edge networks can provide services in proximity to users by deploying computing components and cache at the edge, which can effectively decrease service delay. However, the coexistence of heterogeneous services and the sharing of limited resources lead to the competition between various services for multiple resources. This paper considers two typical heterogeneous services: computing services and content delivery services, in order to properly configure resources, it is crucial to develop an effective offloading and caching strategies. Considering the high energy consumption of 5G base stations, this paper considers the hybrid energy supply model of traditional power grid and green energy. Therefore, it is necessary to design a reasonable association mechanism which can allocate more service load to base stations rich in green energy to improve the utilization of green energy. This paper formed the joint optimization problem of computing offloading, caching and resource allocation for heterogeneous services with the objective of minimizing the on-grid power consumption under the constraints of limited resources and QoS guarantee. Since the joint optimization problem is a mixed integer nonlinear programming problem that is impossible to solve, this paper uses deep reinforcement learning method to learn the optimal strategy through a lot of training. Extensive simulation experiments show that compared with other schemes, the proposed scheme can allocate resources to heterogeneous service according to the green energy distribution which can effectively reduce the traditional energy consumption.

• International Journal of Computer Science & Network Security
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• 제21권6호
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• pp.107-118
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• 2021

The deployment of 5G is in full swing, with a significant yearly growth in the data traffic expected to reach 26% by the year and data consumption to reach 122 EB per month by 2022 [10]. In parallel, the idea of smart cities has been implemented by various governments and private organizations. One of the main objectives of 5G deployment is to help develop and realize smart cities. 5G can support the enhanced data delivery requirements and the mass connection requirements of a smart city environment. However, for specific high-demanding applications like tactile Internet, transportation, and augmented reality, the cloud-based 5G infrastructure cannot deliver the required quality of services. We suggest using multi-access edge computing (MEC) technology for smart cities' environments to provide the necessary support. In cloud computing, the dependency on a central server for computation and storage adds extra cost in terms of higher latency. We present a few scenarios to demonstrate how the MEC, with its distributed architecture and closer proximity to the end nodes can significantly improve the quality of services by reducing the latency. This paper has surveyed the existing work in MEC for 5G and highlights various challenges and opportunities. Moreover, we propose a unique framework based on the use of MEC for 5G in a smart city environment. This framework works at multiple levels, where each level has its own defined functionalities. The proposed framework uses the MEC and introduces edge-sub levels to keep the computing infrastructure much closer to the end nodes.

• International Journal of Computer Science & Network Security
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• 제22권1호
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• pp.305-311
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• 2022

This research aimed to provide a profound description of the practices of social media users in the Kingdom of Saudi Arabia (KSA), specifically the users of Facebook^® (FB) and Snapchat^® (SC), the reasons for these practices, decisions made, and the people involved. Such research would be of significant help to designers and policymakers of social media applications in understanding user practices when using social media applications and the reasons for such practices in the KSA. This better comprehension would be of significant help in improving current applications and creating new ones. According to the data analysis, there was a clear preference for SC over FB in the KSA. Most participants with SC accounts were described as very active users, accessing their accounts at least once a day compared to FB users. The users were led by this high preference for SC to create new words derived from the name of the application and use them in daily life. We showed our experience of carrying out a study in which the main objective was to collect factual empirical data from participants about their daily usage of social media applications while considering the unique cultural settings in the KSA. Mixed quantitative and qualitative methods were used to triangulate the data, increasing its trustworthiness and validity. Multiple perspectives were obtained using various data collection methods. Therefore, conclusions would not be confounded with limitations of any particular methodology or with conditions of any collection rounds. This research would constitute a valuable guide for researchers intending to use methods with male and female informants from different cultures, preparing them for potential challenges and suggesting possible solutions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권6호
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• pp.1892-1912
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• 2022

With the massive demand and growth of cloud computing, virtualization plays an important role in providing services to end-users efficiently. However, with the increase in services over Cloud Computing, it is becoming more challenging to manage and run multiple Virtual Machines (VMs) in Cloud Computing because of excessive power consumption. It is thus important to overcome these challenges by adopting an efficient technique to manage and monitor the status of VMs in a cloud environment. Reduction of power/energy consumption can be done by managing VMs more effectively in the datacenters of the cloud environment by switching between the active and inactive states of a VM. As a result, energy consumption reduces carbon emissions, leading to green cloud computing. The proposed Efficient Dynamic VM Scheduling approach minimizes Service Level Agreement (SLA) violations and manages VM migration by lowering the energy consumption effectively along with the balanced load. In the proposed work, VM Scheduling for Efficient Dynamically Migrated VM (VMS-EDMVM) approach first detects the over-utilized host using the Modified Weighted Linear Regression (MWLR) algorithm and along with the dynamic utilization model for an underutilized host. Maximum Power Reduction and Reduced Time (MPRRT) approach has been developed for the VM selection followed by a two-phase Best-Fit CPU, BW (BFCB) VM Scheduling mechanism which is simulated in CloudSim based on the adaptive utilization threshold base. The proposed work achieved a Power consumption of 108.45 kWh, and the total SLA violation was 0.1%. The VM migration count was reduced to 2,202 times, revealing better performance as compared to other methods mentioned in this paper.

• Smart Structures and Systems
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• 제30권6호
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• pp.613-626
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• 2022

Guaranteeing the quality and integrity of structural health monitoring (SHM) data is very important for an effective assessment of structural condition. However, sensory system may malfunction due to sensor fault or harsh operational environment, resulting in multiple types of data anomaly existing in the measured data. Efficiently and automatically identifying anomalies from the vast amounts of measured data is significant for assessing the structural conditions and early warning for structural failure in SHM. The major challenges of current automated data anomaly detection methods are the imbalance of dataset categories. In terms of the feature of actual anomalous data, this paper proposes a data anomaly detection method based on data-level and deep learning technique for SHM of civil engineering structures. The proposed method consists of a data balancing phase to prepare a comprehensive training dataset based on data-level technique, and an anomaly detection phase based on a sophisticatedly designed network. The advanced densely connected convolutional network (DenseNet) and Transformer encoder are embedded in the specific network to facilitate extraction of both detail and global features of response data, and to establish the mapping between the highest level of abstractive features and data anomaly class. Numerical studies on a steel frame model are conducted to evaluate the performance and noise immunity of using the proposed network for data anomaly detection. The applicability of the proposed method for data anomaly classification is validated with the measured data of a practical supertall structure. The proposed method presents a remarkable performance on data anomaly detection, which reaches a 95.7% overall accuracy with practical engineering structural monitoring data, which demonstrates the effectiveness of data balancing and the robust classification capability of the proposed network.